import os
import paddle
"""
Logging utils
"""
import warnings
from threading import Thread
import pkg_resources as pkg
from utils.general import colorstr, emojis
from utils.loggers.wandb.wandb_utils import WandbLogger
from utils.plots import plot_images, plot_results
from utils.torch_utils import de_parallel
LOGGERS = 'csv', 'tb', 'wandb'
RANK = int(os.getenv('RANK', -1))
try:
    import wandb
    wandb = None
    assert hasattr(wandb, '__version__')
    if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.12'
        ) and RANK in [0, -1]:
        wandb_login_success = wandb.login(timeout=30)
        if not wandb_login_success:
            wandb = None
except (ImportError, AssertionError):
    wandb = None


class Loggers:

    def __init__(self, save_dir=None, weights=None, opt=None, hyp=None,
        logger=None, include=LOGGERS):
        self.save_dir = save_dir
        self.weights = weights
        self.opt = opt
        self.hyp = hyp
        self.logger = logger
        self.include = include
        self.keys = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',
            'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5',
            'metrics/mAP_0.5:0.95', 'val/box_loss', 'val/obj_loss',
            'val/cls_loss', 'x/lr0', 'x/lr1', 'x/lr2']
        for k in LOGGERS:
            setattr(self, k, None)
        self.csv = True
        if not wandb:
            prefix = colorstr('Weights & Biases: ')
            s = (
                f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)"
                )
            print(emojis(s))
        s = self.save_dir
        if 'tb' in self.include and not self.opt.evolve:
            prefix = colorstr('TensorBoard: ')
            self.logger.info(
                f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/"
                )
>>>>>>            self.tb = torch.utils.tensorboard.SummaryWriter(str(s))
        if wandb and 'wandb' in self.include:
            wandb_artifact_resume = isinstance(self.opt.resume, str
                ) and self.opt.resume.startswith('wandb-artifact://')
            run_id = paddle.load(path=str(self.weights)).get('wandb_id'
                ) if self.opt.resume and not wandb_artifact_resume else None
            self.opt.hyp = self.hyp
            self.wandb = WandbLogger(self.opt, run_id)
        else:
            self.wandb = None

    def on_pretrain_routine_end(self):
        paths = self.save_dir.glob('*labels*.jpg')
        if self.wandb:
            self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for
                x in paths]})

    def on_train_batch_end(self, ni, model, imgs, targets, paths, plots,
        sync_bn):
        if plots:
            if ni == 0:
                if not sync_bn:
                    with warnings.catch_warnings():
                        warnings.simplefilter('ignore')
>>>>>>                        self.tb.add_graph(torch.jit.trace(de_parallel(model
                            ), imgs[0:1], strict=False), [])
            if ni < 3:
                f = self.save_dir / f'train_batch{ni}.jpg'
                Thread(target=plot_images, args=(imgs, targets, paths, f),
                    daemon=True).start()
            if self.wandb and ni == 10:
                files = sorted(self.save_dir.glob('train*.jpg'))
                self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.
                    name) for f in files if f.exists()]})

    def on_train_epoch_end(self, epoch):
        if self.wandb:
            self.wandb.current_epoch = epoch + 1

    def on_val_image_end(self, pred, predn, path, names, im):
        if self.wandb:
            self.wandb.val_one_image(pred, predn, path, names, im)

    def on_val_end(self):
        if self.wandb:
            files = sorted(self.save_dir.glob('val*.jpg'))
            self.wandb.log({'Validation': [wandb.Image(str(f), caption=f.
                name) for f in files]})

    def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
        x = {k: v for k, v in zip(self.keys, vals)}
        if self.csv:
            file = self.save_dir / 'results.csv'
            n = len(x) + 1
            s = '' if file.exists() else ('%20s,' * n % tuple(['epoch'] +
                self.keys)).rstrip(',') + '\n'
            with open(file, 'a') as f:
                f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(
                    ',') + '\n')
        if self.tb:
            for k, v in x.items():
                self.tb.add_scalar(k, v, epoch)
        if self.wandb:
            self.wandb.log(x)
            self.wandb.end_epoch(best_result=best_fitness == fi)

    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
        if self.wandb:
            if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch
                ) and self.opt.save_period != -1:
                self.wandb.log_model(last.parent, self.opt, epoch, fi,
                    best_model=best_fitness == fi)

    def on_train_end(self, last, best, plots, epoch, results):
        if plots:
            plot_results(file=self.save_dir / 'results.csv')
        files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for
            x in ('F1', 'PR', 'P', 'R')]]
        files = [(self.save_dir / f) for f in files if (self.save_dir / f).
            exists()]
        if self.tb:
            import cv2
            for f in files:
                self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1],
                    epoch, dataformats='HWC')
        if self.wandb:
            self.wandb.log({'Results': [wandb.Image(str(f), caption=f.name) for
                f in files]})
            if not self.opt.evolve:
                wandb.log_artifact(str(best if best.exists() else last),
                    type='model', name='run_' + self.wandb.wandb_run.id +
                    '_model', aliases=['latest', 'best', 'stripped'])
                self.wandb.finish_run()
            else:
                self.wandb.finish_run()
                self.wandb = WandbLogger(self.opt)
